Ten Things to Know About Canadian Metropolitan Areas: A Synthesis of Statistics Canada's Trends and Conditions in Census Metropolitan Areas Series
Bibliographic record
Abstract
The "Trends and Conditions in Census Metropolitan Areas" series of reports provides key background information on Canadian census metropolitan areas (CMAs) for the period 1981 to 2001. Based primarily on census data, this series provides substantial information and analysis on several topics: low income, health, immigration, culture, housing, labour markets, industrial structure, mobility, public transit and commuting, and Aboriginal people. This final assessment summarizes the major findings of the eight reports and evaluates what has been learned. It points out that the series has three key contributions. First, it details how place matters. Census metropolitan areas differ greatly in many indicators, and their economic and social differences are important factors that define them. Accordingly, policy prescriptions affecting cities may need to reflect this diversity. Second, the series contributes substantially to the amount of data and analysis needed to make accurate policy assessments of what may be ailing in Canada's largest cities and where each problem is most acute. Third, it provides benchmarks against which future data 'most notably data from the 2006 Census' can be examined. This summary also briefly discusses some subjects which were not covered in the series, identifying these as data gaps, or areas where more research is needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.038 | 0.131 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".